Color Infrared Research Articles

Impact of multiple factors on the use of an UAV-mounted infrared thermography method for detection of debonding in facade tiles

Anomalies detection of adhesive tiling systems on the building facade plays a crucial role in structural healthy of buildings. Among defects, the debonding is a major potential threat due to its invisibility and universality. In recent years, there has been a growing trend in utilizing Unmanned Aerial Vehicles (UAVs) equipped with infrared lenses to acquire image data of facade tiles. Numerous previous studies mainly concentrate on the improvement of detection accuracy, and fewer studies investigated the physical range and lighting conditions when capturing infrared photographs using UAVs. This study based on experiments analyses the effects of three operating conditions factors, namely angle, distance, and solar illumination, on the detection effectiveness of tiles debonding by means of calculating the F2 score. There are some reasonable proposals for improving data quality and planning the flight route when using visual methods to detect the debonding defect of ceramic tiles on the building facade.

High resolution remote sensing for biodiversity assessment and monitoring: A case study of dominant tree species in an old-growth forest

Biodiversity assessment and monitoring in old-growth forests (OGFs) is crucial for their effective management and conservation amongst various natural and human-induced challenges. In the present study, the spatial and temporal species diversity were assessed in the mixed stands of Białowieża Forest (BF), a renowned UNESCO world heritage site recognized as an OGF. The BF has encountered challenges due to pest outbreaks peaking in 2016–2017 with adverse impacts on species composition. Utilizing airborne laser scanning (ALS) and color infrared (CIR) datasets from 2015 and 2019 (prior and following the peak, respectively), 30 variables were extracted and employed as input for the Random Forest algorithm to identify 14 species of broadleaved and coniferous individuals at plot levels (458 plots) within the BF. We calculated biodiversity indices (including α, β, and γ diversity), Importance Value Index (IVI), bivariate scale-dependent density of species, and temporal β diversity indices independently for data acquired from both field observation (FO) and species maps obtained through remote sensing analysis (RSA). The study found no significant differences in α diversity indices between the FO and RSA across the BF, however, notable discrepancies existed in species richness. Limited species detection was attributed to low species frequency and therefore, insufficient reference individuals. Average β and γ diversities from both datasets showed no significant differences. The RSA accurately identified top species and their changes in terms of IVI. Additionally, the RSA explored scale-dependent density changes of broadleaved individuals around conifers. Consistency was observed between FO and RSA in identifying dominance shifts from losses to gains within the BF communities. The comparison of diversity indices obtained from FO and RSA suggests that the RSA provided comparable results to FO in capturing the diversity of dominant tree species within the BF, providing a promising approach for advancing our understanding of forest biodiversity in OGFs.

Enhancing Building Point Cloud Reconstruction from RGB UAV Data with Machine-Learning-Based Image Translation.

The performance of three-dimensional (3D) point cloud reconstruction is affected by dynamic features such as vegetation. Vegetation can be detected by near-infrared (NIR)-based indices; however, the sensors providing multispectral data are resource intensive. To address this issue, this study proposes a two-stage framework to firstly improve the performance of the 3D point cloud generation of buildings with a two-view SfM algorithm, and secondly, reduce noise caused by vegetation. The proposed framework can also overcome the lack of near-infrared data when identifying vegetation areas for reducing interferences in the SfM process. The first stage includes cross-sensor training, model selection and the evaluation of image-to-image RGB to color infrared (CIR) translation with Generative Adversarial Networks (GANs). The second stage includes feature detection with multiple feature detector operators, feature removal with respect to the NDVI-based vegetation classification, masking, matching, pose estimation and triangulation to generate sparse 3D point clouds. The materials utilized in both stages are a publicly available RGB-NIR dataset, and satellite and UAV imagery. The experimental results indicate that the cross-sensor and category-wise validation achieves an accuracy of 0.9466 and 0.9024, with a kappa coefficient of 0.8932 and 0.9110, respectively. The histogram-based evaluation demonstrates that the predicted NIR band is consistent with the original NIR data of the satellite test dataset. Finally, the test on the UAV RGB and artificially generated NIR with a segmentation-driven two-view SfM proves that the proposed framework can effectively translate RGB to CIR for NDVI calculation. Further, the artificially generated NDVI is able to segment and classify vegetation. As a result, the generated point cloud is less noisy, and the 3D model is enhanced.

TANGENTIAL RETINAL DISPLACEMENT INCREASES AFTER MACULAR PUCKER SURGERY: An Apparent Nonsense.

To measure the tangential retinal displacement and vision before and after macular pucker surgery and study if pars plana vitrectomy with epiretinal membrane peeling allows the reconstitution of previous anatomy or else it results in a different configuration. Retrospective series of patients undergoing pars plana vitrectomy for epiretinal membrane, with >6-month follow-up before and after surgery, complete with best-corrected visual acuity, optical coherence tomography, M-Charts, and infrared retinography. Tangential retinal displacement between earliest visit (T E ), time of surgery (T 0 ), and latest available visit (T L ) of the examined retina, concentric circles at 0.5, 1.5, and 4.5 mm radii, and the central horizontal and vertical meridians were measured. Tangential displacement was calculated as the optical flow of consecutive infrared photographs. The study comprised 32 patients: 15 men and 17 women. Average preoperative and postoperative follow-up were 23.4 ± 27.9 months and 19.2 ± 11.8 months, respectively. Best-corrected visual acuity reduced before surgery (0.69 ± 0.16 Snellen to 0.46 ± 0.17; P < 0.001) and increased after (0.866 ± 0.16 Snellen; P < 0.001). Horizontal and vertical metamorphopsia increased between before surgery but only horizontal metamorphopsia significantly reduced after. Average tangential displacement before surgery was 35.6 ± 29.9 µ m versus 56.6 ± 41.3 µ m after ( P = 0.023). Preoperative and postoperative displacement within the fovea was less than over the entire area ( P < 0.001). Retinal tangential displacement between diagnosis and surgery (T E - T 0 ) is less than the displacement occurring after surgery (T 0 - T L ). Postoperative displacement does not represent the restoration of the anatomy existing before the disease ensued but rather the resulting equilibrium of newly deployed forces.

DETECTION OF CRIME SCENE OBJECTS FOR EVIDENCE ANALYSIS USING DEEP LEARNING TECHNIQUES

Research on the detection of objects at crime scenes has flourished in the last two decades. Researchers have been concentrating on colour pictures, where lighting is a crucial component, since this is one of the most pressing issues in computer vision, with applications spanning surveillance, security, medicine, and more. However, nighttime monitoring is crucial since most security problems cannot be seen by the naked eye. That's why it's crucial to record a dark scene and identify the things at a crime scene. Even when its dark out, infrared cameras are indispensable. Both military and civilian sectors will benefit from the use of such methods for nighttime navigation. On the other hand, IR photographs have issues with poor resolution, lighting effects, and other similar issues. Surveillance cameras with infrared (IR) imaging capabilities have been the focus of much study and development in recent years. This research work has attempted to offer a good model for object recognition by using IR images obtained from crime scenes using Deep Learning. The model is tested in many scenarios including a central processing unit (CPU), Google COLAB, and graphics processing unit (GPU), and its performance is also tabulated.

Visibly Transparent Multifunctional Metascreen for Tunable Infrared Coloration and Microwave Transmission

AbstractMetascreens are remarkable optical materials that have attracted significant research interest owing to their ability to manipulate electromagnetic waves by selectively transmitting, reflecting, and absorbing specific wavelengths. The present study introduces a new metascreen concept called visibly transparent multifunctional metascreen (VTMM), which is capable of simultaneously transmitting visible light, selectively transmitting microwaves, and modulating infrared (IR) emissions, all through a single surface. The VTMM is fabricated by patterning a thin metal film in a square‐loop pattern on a transparent substrate, resulting in frequency‐selective transmission characteristics in the X‐band and visible transparency. Before demonstrating independent control of the visible and IR coloration of the VTMM by varying the metal film thickness and using color backgrounds, the visible and IR coloration characteristics of the ultrathin metal film on glass (MFG) are analyzed. Based on the findings, the VTMM preserves optical images behind the metascreen while reducing the average IR signature from 89.0 to 69.3 W m−2 sr‐1, with up to 23.5% reduction compared to a glass substrate, as the metal film thickness increases from 40 to 100 nm is confirmed. Furthermore, the designed selective transmission characteristics are shown to remain consistent across varying metal film thicknesses.

Determining the Optimal Sample Size for Assessing Crown Damage on Color Infrared (CIR) Aerial Photographs

One of the priorities in sustainable forest management is monitoring the health status of trees and stands. From the aspect of remote sensing (RS), the best way of doing this is by interpreting color infrared (CIR) aerial photographs; however, this raises the issue of sample size. For this reason, to apply this method in practice, it is indispensable to determine an appropriate sample size to ensure sufficient reliability of the health status assessment of trees in CIR aerial photographs. This research was conducted in lowland forests of pedunculate oak in Croatia. To determine damage in the photographs of the main tree species, a systematic sample with varying dot grid densities—100 × 100 m, 200 × 200 m, 300 × 300 m, 500 × 500 m, 1000 × 1000 m—was used with combinations of different numbers of interpreted trees per sample. Damage indicators were also calculated based on tree distributions obtained by interpreting four trees, two trees and one tree in different sample sizes. The results of the testing showed that there were no statistically significant differences between different sample densities and numbers of interpreted trees in relation to mean damage assessment. Regardless of the fact that there were no statistically significant differences during damage assessment, it was found that by lowering sample densities, starting with 200 × 200 m, the number of trees and the number of sample points per particular sub-compartment significantly decreased, and so did the desired accuracy. Consequently, the participation (distribution) of particular species and damage degrees in the sample were lost, which significantly affected the overall tree health assessment. In contrast, grid densities of 100 × 100 m with one interpreted tree at the raster point proved to be the optimal sample size. This confirms the fact found in earlier research, that is, that the selected sample should have several spatially well-distributed points with a smaller number of trees in the point, and samples with larger numbers of trees in a smaller number of points should be avoided.

Towards complete tree crown delineation by instance segmentation with Mask R–CNN and DETR using UAV-based multispectral imagery and lidar data

Precise single tree delineation allows for a more reliable determination of essential parameters such as tree species, height and vitality. Methods of instance segmentation are powerful neural networks for detecting and segmenting single objects and have the potential to push the accuracy of tree segmentation methods to a new level. In this study, two instance segmentation methods, Mask R–CNN and DETR, were applied to precisely delineate single tree crowns using multispectral images and images generated from UAV lidar data. The study area was in Bavaria, 35 km north of Munich (Germany), comprising a mixed forest stand of around 7 ha characterised mainly by Norway spruce (Picea abies) and large groups of European beeches (Fagus sylvatica) with 181–236 trees per ha. The data set, consisting of multispectral images and lidar data, was acquired using a Micasense RedEdge-MX dual camera system and a Riegl miniVUX-1UAV lidar scanner, both mounted on a hexacopter (DJI Matrice 600 Pro). At an altitude of approximately 85 m, two flight missions were conducted at an airspeed of 5 m/s, leading to a ground resolution of 5 cm and a lidar point density of 560 points/m2. In total, 1408 trees were marked by visual interpretation of the remote sensing data for training and validating the classifiers. Additionally, 125 trees were surveyed by tacheometric means used to test the optimized neural networks. The evaluations showed that segmentation using only multispectral imagery performed slightly better than with images generated from lidar data. In terms of F1 score, Mask R–CNN with color infrared (CIR) images achieved 92% in coniferous, 85% in deciduous and 83% in mixed stands. Compared to the images generated by lidar data, these scores are the same for coniferous and slightly worse for deciduous and mixed plots, by 4% and 2%, respectively. DETR with CIR images achieved 90% in coniferous, 81% in deciduous and 84% in mixed stands. These scores were 2%, 1%, and 2% worse, respectively, compared to the lidar data images in the same test areas. Interestingly, four conventional segmentation methods performed significantly worse than CIR-based and lidar-based instance segmentations. Additionally, the results revealed that tree crowns were more accurately segmented by instance segmentation. All in all, the results highlight the practical potential of the two deep learning-based tree segmentation methods, especially in comparison to baseline methods.

The use of in situ non-invasive techniques as powerful tools in the investigation of eighteenth century Chinese wallpapers from the National Museum of Ancient Art—Lisbon

The National Museum of Ancient Art in Lisbon (MNAA) hosts the most important Portuguese public collection of art. Among its different types of artworks, a set of six Chinese wallpaper panels from the eighteenth century is present; they represent the production of porcelain and have been donated to the Museum in 1949. Despite the large interest that has raised around these kinds of artefacts, few technical studies have been carried out to date on Chinese wallpapers. In this study, a non-invasive investigation of the wallpaper panels from the MNAA by means of portable devices is described. More specifically, the combined use of technical photography (namely UVF, Vis and IR photography), Vis-NIR-FORS and EDXRF allowed to carry out a preliminary diagnostic survey, which higlighted an improper handling of the wallpapers prior to their donation to the Museum, and poor conservation conditions of the paper sheets. Furthermore, it allowed for the identification of the painting technique, the main pigments, and their distribution; the presence of inorganic pigments (such as vermilion and lead white) and organic dyes (e.g. anthraquinone-based red dyes and indigo), used both as overlapping layers and mixtures, has been determined.

Iveron Icon of the Mother of God: reconstruction based on physical–chemical tests as an element of conservation

Recently art conservators tend to wonder which of the possible intervention paths to undertake while planning a conservation project and how deep their interference with the original substance should be. Limiting intervention to ceasing the ongoing degradation process and securing the object for the future without restoring any missing parts of the composition is just one of the many available approaches. Making a brand new copy of the painting where all the missing parts could be fully reconstructed and exposing it along with the original is a good alternative to traditional restoration practice. This method has been used by Danuta Stępień during conservation of a Russian icon entitled “Our Lady of Iver” (Moscow, 1908–1917, egg tempera on a wooden panel partially covered by a silver oklad, 10.8 × 13.2 × 0.8 cm). The silver oklad was executed by the workshop of Smirnow. Technology and materials have been identified via different analytical techniques: VIS, UV, IR and X-ray photography, microscopic photography, SEM–EDS analysis and FTIR. Specific medical radiological instruments have also been used as non-invasive analytical tools. The following materials have been identified: yellow ochre, cinnabar, cadmium red, red iron oxides, silver, gold and copper. The oklad is made of an Ag-rich silver–copper alloy. Its whole surfaces are richly engraved with traces of gold detected inside the engravings. Based on this fact, an assumption has been made that originally the upper part of the oklad might have been entirely covered with a layer of gold. The information gathered during research allowed to execute a fine copy of the icon using similar materials and techniques. An alternative to classic restoration approach is to create a faithful but fully reconstructed copy that could be exposed along with the original painting. This solution not only assures maximum respect for the original substance but also allows the recipients to better understand its history and technique.

In Situ Implementation of Imaging Techniques Reveals Key Motifs in the Early Byzantine Wall Painting of Christ’s Baptism at Shivta, Israel

Following the rediscovery of the Early Byzantine wall painting of Christ’s baptism in the northern church of Shivta in southern Israel, the painting was investigated with different imaging techniques in order to clarify the scene and to find additional painted motifs and hidden colors and patterns. Besides high-quality photography from close distance on a scaffolding at 3.5 m height, thus at the level of the painting, Near IR photography (NIR), UV wavelengths microscopy, and Visible Induced Luminescence (VIL) imaging were all conducted in order to collect new information on the painting. The combination of several imaging techniques provided new and surprising details on elements of the painting, especially revealing blue color around the head and shoulders of Christ, most probably reminiscent of the River Jordan where the baptism took place.

Geomorphometric and Geophysical Constraints on Outlining Drained Shallow Mountain Mires

Long-term draining of peatlands results in transformation of vegetation and obliteration of their morphological features. In many areas, efforts are made to restore the original ecosystems and increase their water retention potential. Using combined analyses of a LiDAR-based digital terrain model (DTM), colour-infrared (CIR) imagery data, ground-penetrating radar (GPR) data and electrical resistivity tomography (ERT) data, we tested the applicability of these methods in outlining the extent and subsurface structure of drained mires located in the Stolowe Mountains National Park area, Poland. The LiDAR-DTMs enabled parameterisation of physiographic features of the mires and determination of their extent, runoff directions and potential waterlogging areas. CIR analysis enabled classification of vegetation types. GPR prospecting revealed the bedrock morphology, thickness and internal structure of the peat deposits, showing that this technique can also provide data on variability in the decomposition of phytogenic deposits. The obtained ERT sections indicate both the thickness of peat deposits and variability in the bedrock internal structure. The results show that integrated analyses of data obtained with different methods can be an effective tool in outlining the original extent of peatlands, with potential application in the planning of peatland ecosystem restitution.

Detection of standing retention trees in boreal forests with airborne laser scanning point clouds and multispectral imagery

Abstract In a landscape consisting primarily of intensive forestry interspersed with some protected areas, multifunctional forestry with retention trees can play a crucial role in nature conservation. Accurate mapping of retention trees is important for guiding landscape‐level conservation and forest management and improving landscape connectivity. Sizeable dead and living retention trees play a particularly important ecological role but even their large‐scale inventory is often intensive through field work and/or inaccurate. We aimed to detect and classify retention trees using the novel nationwide Finnish airborne laser scanning (ALS) data (~5 pulses/m2) in conjunction with unrectified colour‐infrared (CIR) aerial imagery. Applying photogrammetric principles, we added spectral information from the CIR imagery to the ALS‐derived point cloud. For a training dataset of 160 retention trees from 19 stands and a geographically separate validation dataset of 79 trees from eight stands, we segmented trees via individual tree detection (ITD), removed most trees belonging to the regenerating vegetation layer, and classified trees into living conifers, living broadleaves and dead trees by linear discriminant analysis. The detection rate via ITD differed considerably for dead and living trees, with 41.7% of all dead and 83.8% of all living trees being detected with relatively low commission error rates. Dead trees with smaller diameters and heights were more likely missed, while grouping caused living tree omission. For classification into living conifers, living broadleaves and dead trees, an overall accuracy of 67.3% was achieved in training and 71.2% in validation using only ALS‐derived metrics. When adding spectral metrics, the overall accuracies were 79.6% and 61.0% for training and validation respectively. Our findings imply that wall‐to‐wall large‐scale high density ALS data can be used to detect retention trees rather accurately—even larger dead trees—and that metrics derived solely from ALS data can accurately classify detected retention trees into living conifers, living broadleaves and dead trees. Considering the ecological value of retention trees, our results are promising and indicate that ALS data of the studied pulse density are a cost‐effective option for large area mapping of retention trees in countries with such data available.

A Comparative Assessment of Hygienic Behavior of Carniolan (Apismelliferacarnica Pollmann) and Yemeni (Apismellifera jemenitica Ruttner) Honeybees Using Infra-Red Photography Video Recording

The use of infra-red photography video recording is very useful for conducting behavioristic studies of honeybees against many brood diseases. The removal of dead or diseased brood from capped cells by honeybee workers is a heritable trait that confers colony-level resistance. This work aimed to compare the hygienic behavior of the native (Yemeni bees, A. mellifera jemenitica) and the exotic (Carniolan bees, A. m. carnica) honeybee races in Saudi Arabia using an infra-red photography video recording. In addition, hygienic behavior towards the related and non-related combs was examined. Therefore, it is possible to obtain honeybee colonies with greater disease resistance. The pin-killing method and infra-red photography video recording were used for the evaluation of hygienic behavior in colonies of the two races. Significant differences in hygienic behavior between the two races were detected at the beginning of the experiment. Under the environmental conditions of eastern Saudi Arabia, the Yemeni honeybee colonies showed a higher number of uncapped and cleaned cells containing dead brood in either the brood comb from the same colony, or the brood comb from the same race but a different colony, or brood comb from a different race. It was concluded that the honeybee’s ability to detect and clean the dead brood from comb cells can be correlated with race and it is more efficient for the non-related individuals of the same race than from a related or another race. The outstanding performance of a few individuals in the expression of various traits indicates their usefulness in carrying out breeding programs for Varroa resistance.

The forger's identikit: A multi-technique characterization of Pippo Oriani's fake paintings

This work is part of a multi-disciplinary study aimed at creating the first scientific database to support the future authentication of paintings on the art market. In particular, five fake paintings attributed to the Italian painter Pippo Oriani (1909–1972) have been characterized. Since no scientific literature concerning the material composition used by the artist is available, the five paintings were studied with a multi-technique approach aiming at better understanding the forgery process. The obtained results can be used in the future as a first step to support the authentication of Oriani's artworks on the art market. In particular, it allows to evaluate the consistency of data obtained from future scientific investigations on Oriani's artwork with the forgery process reported in the present database. Non-invasive diagnostic tools, such as UV imaging, IR and IR-false colour photography, XRF, μ-Raman and FTIR spectroscopies have been used to fully characterize the fake paintings. The combination of these techniques led to obtaining useful information on the support preparation method, the pictorial materials, and the execution technique of the artist/forger(s).A wide range of inorganic pigments and organic dyes have been detected, together with the use of oil and wax as binders. The results revealed a strong similarity among the different paintings, thus supporting the idea that the forger(s) knew very well the original Oriani's pictorial technique.The detailed identification of all the materials used in the forgery process for Oriani's fake paintings will help to support the authentication of other ambiguous paintings in future cases.

Enhancing Wheat Disease Diagnosis in a Greenhouse Using Image Deep Features and Parallel Feature Fusion.

Since the assessment of wheat diseases (e.g., leaf rust and tan spot) via visual observation is subjective and inefficient, this study focused on developing an automatic, objective, and efficient diagnosis approach. For each plant, color, and color-infrared (CIR) images were collected in a paired mode. An automatic approach based on the image processing technique was developed to crop the paired images to have the same region, after which a developed semiautomatic webtool was used to expedite the dataset creation. The webtool generated the dataset from either image and automatically built the corresponding dataset from the other image. Each image was manually categorized into one of the three groups: control (disease-free), disease light, and disease severity. After the image segmentation, handcrafted features (HFs) were extracted from each format of images, and disease diagnosis results demonstrated that the parallel feature fusion had higher accuracy over features from either type of image. Performance of deep features (DFs) extracted through different deep learning (DL) models (e.g., AlexNet, VGG16, ResNet101, GoogLeNet, and Xception) on wheat disease detection was compared, and those extracted by ResNet101 resulted in the highest accuracy, perhaps because deep layers extracted finer features. In addition, parallel deep feature fusion generated a higher accuracy over DFs from a single-source image. DFs outperformed HFs in wheat disease detection, and the DFs coupled with parallel feature fusion resulted in diagnosis accuracies of 75, 84, and 71% for leaf rust, tan spot, and leaf rust + tan spot, respectively. The methodology developed directly for greenhouse applications, to be used by plant pathologists, breeders, and other users, can be extended to field applications with future tests on field data and model fine-tuning.

Identification of pigments used in a Qajar manuscript from Iran by using atomic and molecular spectroscopy and technical photography methods

The present study aimed to identify pigments used in writing and decorating a Qur'an manuscript related to the early years of the Qajar era, a royal dynasty ruling over Iran from 1789 to 1925. The pigments were analysed using µ-XRF, SEM–EDS, Raman, and FTIR spectroscopy. Also, various technical photography methods were used to study the pigments including visible-reflected (VIS), infrared photography (IR), ultraviolet-reflected (UVR), and ultraviolet-induced visible luminescence (UVL) along with infrared false color (IRFC) and ultraviolet false color (UVFC) methods. Carbon black, red lead, and indigo were identified as black ink, orange, and blue pigments, respectively. A mixture of red lead and vermilion was also detected in the red/orange ink. White lead and calcite were also used as additives in blue color. A cochineal-based pigment was also used as red ink in some parts of the manuscript. However, the evaluation of golden color showed two types pigments of gold and brass alloys in different parts of the manuscript. Copper carboxylate was identified as the product of the destruction of this pigment. According to the results of the study, technical photography methods along with spectroscopic methods are a good tool to examine manuscripts and identify included pigments and reduce the amount of possible error and the need for destructive sampling.

Clinical and Multi-Mode Imaging Features of Eyes With Peripapillary Hyperreflective Ovoid Mass-Like Structures.

PurposeTo observe and analyze the clinical and multi-mode imaging features of eyes with PHOMS, and to introduce two cases of PHOMS which underwent multi-mode imaging.MethodsRetrospective clinical observational study. A total of 26 patients (37 eyes) with hyperreflective structures surrounded by hyporeflective edges around the optic discs who were examined and diagnosed at Shandong Eye Hospital between January 2019 and June 2021 were included in the study. Among these patients, 12 were male and 14 were female. Fifteen were monocular. The average age was 39 years. All patients underwent the following examinations: Best-corrected visual acuity (BCVA), intraocular pressure examinations, slit-lamp anterior segment examinations, indirect ophthalmoscopy, visual field examinations, fundus color photography, fundus autofluorescence (FAF), optical coherence tomography (OCT), and optical coherence tomography angiography (OCTA). Some of the patients were examined with fundus fluorescein angiography (FFA). Clinical data and imaging characteristics from the OCT, OCTA, and FFA were analyzed retrospectively.ResultsWe found the hyperreflective structures surrounded by hyporeflective edges around the optic discs in 37 eyes. EDI-OCT results revealed hyperreflective structures surrounded by hyporeflective edges around the optic discs in all eyes. Typical hyperreflexia lesions occurred around the optic disc, located subretinally and above Bruch's membrane. OCTA revealed that the highly reflective perioptic material also had vascular structures.ConclusionEDI-OCT of PHOMS showed hyperreflective structures surrounded by hyporeflective edges around all of the optic discs. Infra-red photography showed temporal hyperreflexia. These characteristics can be seen in a variety of diseases and may be a relatively common feature revealed by EDI-OCT scanning. These characteristics may also be seen in elderly patients as well as children. PHOMS may be found in optic disc drusen (ODD), tilted disc syndrome (TDS), optic neuritis, ischemic optic neuropathy, and in white dot syndromes. Few patients may be developed into macular neovascularization (MNV). In order to improve the accuracy and robustness of the conclusions and provide better clinical guidance, we need to conduct more comprehensive research in the subsequent clinical work.

The dark halo technique in the oeuvre of Michael Sweerts and other Flemish and Dutch baroque painters. A 17th c. empirical solution to mitigate the optical \u2018simultaneous contrast\u2019 effect?

Although the topic is rarely addressed in literature, a significant number of baroque paintings exhibit dark, halo-like shapes around the contours of the dramatis personae. Close examination of both finished and unfinished works suggests that this intriguing feature was a practical tool that helped the artist in the early painting stages. When applying the final brushwork, the halo lost its function, with some artists undertaking efforts to hide it. Although their visibility might not have been intended by the artists, today this dark paint beneath the surface is partially visible through the upper paint layers. Moreover, the disclosure of many halos using infrared photography (IRP), infrared reflectography (IRR) and macro X-ray fluorescence imaging (MA-XRF), additional to those that can be observed visually, suggests that this was a common and established element of 17th-century painting practice in Western Europe. Building on an existing hypothesis, we argue that halos can be considered as a solution to an optical problem that arose when baroque painters reversed the traditional, 15th- and 16th-century painting sequence of working from background to foreground. Instead, they started with the dominant parts of a composition, such as the face of a sitter. In that case, a temporary halo can provide the essential tonal reference to anticipate the chromatic impact of the final dark colored background on the adjacent delicate carnations. In particular, we attempt to clarify the prevalence of dark halos as a response to optical effects such as ‘simultaneous contrast’ and ‘the crispening effect’, described in literature only centuries later. As such, the recently termed ‘ring condition’ can be seen as the present-day equivalent of the ‘halo solution’ that was seemingly empirically or intuitively developed by 17th-century artists. Modern studies in visual perception proves that by laying a black ring around a target color, the optical impact of a surrounding color can be efficiently neutralized. Finally, by delving into works by Michael Sweerts, it becomes clear that resourceful artists might have adapted the halo technique and the underlying principles to their individual challenges, such as dealing with differently colored grounds.

Detection Rates of Northern Bobwhite Coveys Using a Small Unmanned Aerial System-Mounted Thermal Camera

The northern bobwhite (Colinus virginianus; hereafter, bobwhite) requires intensive monitoring to evaluate management efforts and determine harvest rates. However, traditional monitoring techniques (e.g., covey-call surveys) are labor-intensive and imprecise. Small unmanned aerial systems (sUASs) mounted with thermal cameras have demonstrated promise for monitoring multiple avian species and could provide a less intensive and more effective approach to monitoring bobwhite coveys, assuming coveys produce a recognizable heat signature. To assess sUAS monitoring, we evaluated the influence of bobwhite covey size (3, 6, and 12) and cover type (grass, shrub, and forest) on covey detectability by a sUAS equipped with a thermal camera. We hypothesized that forest would have the lowest covey detection due to trees obstructing detection of the thermal signature and that larger covey size would improve covey detection due to the formation of larger, more visibly distinct thermal signatures. We placed groups of known-size, pen-reared bobwhites in steel mesh cages (3, 6, and 12 individuals/cage) in 3 vegetation types (grass, shrub, and forest) among predetermined locations on a private farm in Clay County, Mississippi, USA (3 replicates, 27 total cages). At civil twilight on 5 March 2020, the sUAS flew a systematic route over the cage area at 30 m above ground level, capturing thermal infrared photographs every 5 seconds. To assess detection, we distributed 57 photographs to 31 volunteers and asked them to assign a binary value for detection (1, 0) regarding covey presence in each photograph. Overall true positive rate was 0.551 but improved with increasing covey size. By vegetation type, simulated coveys in grass had the lowest true positive rate by photograph (0.403), followed by forest (0.562) and shrub (0.605). Results indicate that sUASs and thermal camera technology could be a viable method for surveying intact bobwhite coveys, especially if detection of smaller groups and those in denser vegetation improves. As this technology advances, we recommend that future research focus on evaluating the efficacy of this novel methodology through assessing the influence of weather conditions, camera specifications, flight speed, and altitude, as well as assessing machine learning for processing photos.